Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing - Ahmed, M. M.; Kader, Md. Abdul; Kamal Z., Zamli (American Scientific Publisher, 2018)
Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journals have been ranked by their journal impact factor (JIF). However, several impact evaluation methods have been used by different BSD digital systems, such as the citation analysis, G-Index, H-index, i10-index, jurnal impact (JIF), and the Eigenfactor. In this paper, a detailed study of these different impact evaluation methods is shown along with their advantages and disadvantages. From this study, we can say that although the evaluation methods appear highly correlated but they lead to large differences in BSD impact evaluation. We conclude that no one evaluation method is superior and the present research gap is to develop standard rubrics and standard benchmarks in order to evaluate these existing methods. Furthermore, we have hypothetically modeled a new fuzzy granular approach as evolving structural fuzzy model (ESFM) which consider the concept of granular computing. Therefore, information granules exhibit the expressive and functional depiction of the global concept.